Scene Segmentation-Based Luminance Adjustment for Multi-Exposure Image Fusion

2020

IEEE Access

Self Cite

In this paper, we propose a novel hue-correction scheme based on the constant-hue plane in the RGB color space for deep-learning-based color-image enhancement. Existing hue-preserving imageenhancement methods cannot be applied to state-of-the-art enhancement methods such as deep-learningbased ones. Our main contributions are a discussion on the enhancement performance and the hue distortion of existing image-enhancement methods as well as the first attempt to make hue correction applicable to any existing image-enhancement methods including deep-learning-based ones. This novel scheme is carried out on the basis of the constant-hue plane in the RGB color space. In simulations, we first evaluated conventional image-enhancement methods in terms of the enhancement performance and hue distortion by using five objective metrics: the maximally saturated color similarity, the hue difference in CIEDE2000, discrete entropy, HIGRADE, and NIQMC. The experimental results show that recent deep-learning-based methods have a higher enhancement performance but cause images to be hue-distorted. In addition, the proposed scheme is demonstrated to be effective for suppressing hue distortion even under the use of deep-learningbased enhancement methods. Furthermore, it allows us not only to correct hue but also to maintain the performance of image-enhancement methods.

Section: A Image Enhancementmentioning

confidence: 99%

Hue-Correction Scheme Based on Constant-Hue Plane for Deep-Learning-Based Color-Image Enhancement

2020

IEEE Access

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“…For this reason, recent Retinex-based methods [9,10,29] decompose images into reflectance and illumination and then enhance images by manipulating illumination. Additionally, multi-exposure-fusion (MEF)-based single-image enhancement methods were recently proposed [11,12,31]. One of them, a pseudo MEF scheme [12], makes any single image applicable to MEF methods by generating pseudo multi-exposure images from a single image.…”

Section: A) Image Enhancementmentioning

confidence: 99%

Hue-correction scheme considering CIEDE2000 for color-image enhancement including deep-learning-based algorithms

2020

SIP

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In this paper, we propose a novel hue-correction scheme for color-image-enhancement algorithms including deep-learning-based ones. Although hue-correction schemes for color-image enhancement have already been proposed, there are no schemes that can both perfectly remove perceptual hue-distortion on the basis of CIEDE2000 and be applicable to any image-enhancement algorithms. In contrast, the proposed scheme can perfectly remove hue distortion caused by any image-enhancement algorithm such as deep-learning-based ones on the basis of CIEDE2000. Furthermore, the use of a gamut-mapping method in the proposed scheme enables us to compress a color gamut into an output RGB color gamut, without hue changes. Experimental results show that the proposed scheme can completely correct hue distortion caused by image-enhancement algorithms while maintaining the performance of the algorithms and ensuring the color gamut of output images.

“…Although this method can reduce some noise, it does not preserve details in bright areas as with histogram-based methods. Another way for enhancing images is to use a multi-exposure image fusion method by using photos with different exposures [18]- [21].…”

Section: B Image Enhancementmentioning

confidence: 99%

A Noise-aware Enhancement Method for Underexposed Images

Chien

2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)

2019

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A novel method of contrast enhancement is proposed for underexposed images, in which heavy noise is hidden. Under low light conditions, images taken by digital cameras have low contrast in dark or bright regions. This is due to a limited dynamic range that imaging sensors have. For these reasons, various contrast enhancement methods have been proposed so far. These methods, however, have two problems: (1) The loss of details in bright regions due to over-enhancement of contrast.(2) The noise is amplified in dark regions because conventional enhancement methods do not consider noise included in images. The proposed method aims to overcome these problems. In the proposed method, a shadow-up function is applied to adaptive gamma correction with weighting distribution, and a denoising filter is also used to avoid noise being amplified in dark regions. As a result, the proposed method allows us not only to enhance contrast of dark regions, but also to avoid amplifying noise, even under strong noise environments.